Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
Abstract
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years. It is efficient and accurate for sparse scenes, but suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this drawback, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. Through neatly coupling with GreedyNMS without losing efficiency, consistent improvements have been achieved in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In addition, Pairwise-NMS can be integrated into any learning based detectors (Both of Faster-RCNN and DPM detectors are tested in this paper), thus building a bridge between GreedyNMS and end-to-end learning detectors.
Cite
@article{arxiv.1901.03796,
title = {Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes},
author = {Yu Liu and Lingqiao Liu and Hamid Rezatofighi and Thanh-Toan Do and Qinfeng Shi and Ian Reid},
journal= {arXiv preprint arXiv:1901.03796},
year = {2019}
}
Comments
12 pages